| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | September 11, 2019 |
| Latest Amendment Date: | September 11, 2019 |
| Award Number: | 1909866 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Ann Von Lehmen
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 15, 2019 |
| End Date: | August 31, 2022 (Estimated) |
| Total Intended Award Amount: | $149,970.00 |
| Total Awarded Amount to Date: | $149,970.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
104 AIRPORT DR STE 2200 CHAPEL HILL NC US 27599-5023 (919)966-3411 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
NC US 27599-1350 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Networking Technology and Syst |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
High-speed Internet access plays an important role in modern life. It helps narrow the digital divide, enables e-commerce, and provides opportunities for remote work, study, and entertainment. In the US, cable Internet Service Providers (ISPs) are one of the few available infrastructures that can provide high-speed Internet access to US homes, and in many rural areas, they are often the only broadband choice. However, much measurement study has shown that broadband access networks have poor reliability. (Many parts of the cable networks are now decades old.) This work aims to address this problem. If successful, it can significantly improve the reliability of cable broadband networks, thereby improving Internet availability and quality of experience of millions of cable broadband users.
It is challenging to improve the reliability of cable networks because the "last-mile" links that connect a subscriber's home to the Internet are often made of coaxial cables. They are vulnerable to radio frequency interference and suffer from aging-related issues. These problems can manifest themselves as unrecoverable noisy signals, disrupting a subscriber's Internet connectivity. The cable industry currently collects performance data from users' cable modems. However, there is a lack of systematic study on how to use this data to detect and localize network problems and to rank the severity of problems. Existing techniques often lead to an unacceptably high number of false positives in practice. This work will develop algorithms and tools that can help detect and localize faults in cable networks and estimate the performance impact of network faults. Specifically, the work will include 1) algorithms that use collected data to accurately detect faults inside a cable network; 2) algorithms that localize a fault to a geographical location by clustering anomalous data patterns; and 3) algorithms that estimate how many calls an ISP may receive given a network problem as a mechanism to prioritize repairs. In this project, these algorithms will be implemented in software. All algorithms and code resulting from this work will be made publicly available. The researchers anticipate that the algorithms developed in this project are also applicable to WiFi and cellular networks.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The internet has a central and crucial role in today's economy and society more broadly. Broadband internet access allows for the free exchange of information, online education and healthcare, and flexible remote employment opportunities. The value of these opportunities depend crucially on the performance and reliability of the network. In the US and abroad, many households rely on cable broadband networks with DOCSIS technology. The research funded by this award develops algorithms and approaches for performing proactive network maintenance to improve reliability and performance of these networks. Specifically, the funding facilitated close cooperation with cable broadband providers to gather date and work on these issues. The title and abstract for the first publication from this research is provided below.
"CableMon: Improving the Reliability of Cable Broadband Networks via Proactive Network Maintenance."
Cable broadband networks are one of the few “last-mile” broadband technologies widely available in the U.S. Unfortunately, they have poor reliability after decades of deployment. Cable industry proposed a framework called Proactive Network Maintenance (PNM) to diagnose the cable networks. However, there is little public knowledge or systematic study on how to use these data to detect and localize cable network problems. Existing tools in the public domain have prohibitive high false-positive rates. In this paper, we propose CableMon, the first public-domain system that applies machine learning techniques to PNM data to improve the reliability of cable broadband networks. CableMon uses statistical models to generate features from time series data and uses customer trouble tickets as hints to infer abnormal thresholds for these generated features. We use eight-month of PNM data and customer trouble tickets from an ISP to evaluate CableMon’s performance. Our results show that 81.9% of the abnormal events detected by CableMon overlap with at least one customer trouble ticket. This ticket prediction accuracy is four times higher than that of the existing public-domain tools used by ISPs. The tickets predicted by CableMon constitute 23.0% of the total network-related trouble tickets, suggesting that if an ISP deploys CableMon and proactively repairs the faults detected by CableMon, it can preempt those customer calls. Our current results, while still not mature, can already tangibly reduce an ISP’s operational expenses and improve customers’ quality of experience. We expect future work can further improve these results.
Last Modified: 12/20/2023
Modified by: Jonathan W Williams
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